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.5em plus1ex minus.2ex-.5emSingle-scale Multi-scale. In Table REF  we compare our default FPN-based multi-scale detector to a single-scale variant. The single-scale variant simply applies RPN and RoIAlign [1]} to the final \(1/16\) th resolution feature map generated by the ViT backbone. The RoI heads and all other choices are the same between the systems (in particular, note that both use the same hybrid windowed/global attention). We observe that the multi-scale FPN design increases AP\(^\text{box}\)  by \(\scriptstyle \sim \) 1.3-1.7 (, 50.1 48.4), while increasing training and inference time by \(\scriptstyle \sim \) 5 and \(\scriptstyle \sim \) 10% relative, respectively. Multi-scale memory overhead is \(<\) 1%.
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